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The AI Bubble Isn’t Bursting. Workers Are Paying for It Anyway.

The AI Bubble Isn’t Bursting. Workers Are Paying for It Anyway.

Despite years of “bubble” warnings, there has been no dot-com-style crash. Instead, the strain is landing on payrolls. Big Tech is spending roughly $700 billion on AI infrastructure, and the money has to come from somewhere. 60,000Microsoft buyout offers, about a quarter of its staff (reported) 81,747Tech layoffs in Q1 2026 alone **56%**of 2026 layoff events cite AI or automation

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Buyouts are the new layoffs

Last week Microsoft offered voluntary buyouts to as many as 60,000 employees while it pours money into AI. Meta separately moved to cut about 8,000 jobs. Buyouts let companies shrink without the headline of a forced layoff, and they shift the decision onto the employee. The catch, according to workforce researchers, is that the people who take them are often the high performers with the best options elsewhere.

Is it really AI?

Not entirely. Sam Altman has acknowledged some “AI washing,” where firms blame AI for cuts they would have made anyway. An NBER survey found CFOs planning far more AI-linked cuts in 2026, yet the productivity gains that would justify them have not clearly arrived. The pattern looks less like machines replacing workers and more like companies trimming labor to fund the machines.
The bubble hasn’t popped. But if AI revenue doesn’t catch up with AI spending, the first bill is already being paid in jobs.

What to watch

Three signals matter: whether AI revenue grows fast enough to justify capex, whether buyout rounds turn into forced layoffs, and whether productivity data finally shows up. Until then, “bubble or not” is the wrong question. The right one is who absorbs the cost.
Sources: Reuters and CBS News (Microsoft buyouts), Marketplace (buyouts vs. layoffs), Invezz (Q1 tech layoffs, Altman), SkillSyncer tracker via The Interview Guys, NBER via Interview Query. Figures vary by source; Microsoft’s buyout scope has been reported differently across outlets.
Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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